Eval Analyzer

Analyze AILANG evaluation baseline results to identify failure patterns, compare model performance, and generate actionable insights.

Quick Start

Most common usage:

# User says: "Analyze the v0.3.24 eval results"
# This skill will:
# 1. Run eval-analyze to categorize failures (standard eval)
# 2. Run agent KPIs to analyze efficiency (agent eval)
# 3. Generate summary with jq queries
# 4. Identify top failing benchmarks
# 5. Show model performance comparison
# 6. Provide optimization recommendations

For agent evaluation analysis (NEW - optimization focus):

# Step 1: Get efficiency metrics (turns, tokens, cost)
.claude/skills/eval-analyzer/scripts/agent_kpis.sh eval_results/baselines/v0.3.24

# Step 2: Investigate expensive benchmarks
.claude/skills/eval-analyzer/scripts/agent_transcripts.sh eval_results/baselines/v0.3.24 simple_print

# Step 3: Compare Python vs AILANG
./tools/compare_agents.sh eval_results/baselines/v0.3.24

See resources/agent_optimization_guide.md for complete optimization strategies.

When to Use This Skill

Invoke this skill when:

User asks to "analyze eval results", "check benchmarks", "what's failing"
After running an eval baseline
When investigating why benchmark performance changed
User wants to understand failure patterns or model performance
Comparing two versions of AILANG

Key Eval Commands

All commands work on baseline directories like eval_results/baselines/v0.3.16/.

1. Quick Overview - `eval-matrix`

Shows comprehensive statistics with model/language breakdowns.

ailang eval-matrix eval_results/baselines/v0.3.16 0.3.16 | head -60

Shows: Overall stats, per-model performance, per-language breakdown, top error codes.

2. Detailed Analysis - `eval-analyze`

Categorizes failures and can generate design docs for issues.

# Dry run (no design docs, just analysis)
ailang eval-analyze -results eval_results/baselines/v0.3.16 -dry-run

# Full analysis with design doc generation
ailang eval-analyze -results eval_results/baselines/v0.3.16

⚠️ CRITICAL: Must use -results flag, NOT positional argument!

Output: Categorized failures (compile_error, logic_error, runtime_error) with frequency, affected benchmarks, models, and sample errors.

3. Query-Friendly Summary - `eval-summary`

Generates JSONL for easy querying with jq.

ailang eval-summary eval_results/baselines/v0.3.16

Output: eval_results/baselines/v0.3.16/summary.jsonl

4. Compare Versions - `eval-compare`

Shows what changed between two versions.

ailang eval-compare eval_results/baselines/v0.3.15 eval_results/baselines/v0.3.16

5. Fair Comparison (RECOMMENDED) - `fair_comparison.py`

Use this for accurate version comparisons! The eval-compare command may include duplicates or different model sets. This script normalizes data for apple-to-apples comparison.

.claude/skills/eval-analyzer/scripts/fair_comparison.py

What it does:

Deduplicates runs (keeps last run per benchmark+model)
Filters to dev models only (gpt5-mini, claude-haiku-4-5, gemini-2-5-flash)
AILANG only (ignores Python results)
Shows net fixes vs regressions
Per-model breakdown

Output:

v0.4.0: 56/123 = 45.5%
v0.4.2: 59/123 = 48.0%
Delta:  +3 (+2.4pp)

✅ Fixed:   11 benchmarks
❌ Broken:  8 benchmarks
NET:       +3 benchmarks

When to use: Before making decisions based on eval results (e.g., reverting changes, merging PRs).

6. Validate Results - `validate_eval_results.py`

Check for output corruption and race conditions in eval results.

python3 tools/validate_eval_results.py eval_results/baselines/v0.4.2

Checks:

Output corruption (fibonacci outputting "All results equal", etc.)
Duplicate runs for same benchmark+model
Code hash validation (if available)
Success rate statistics

When to use: After running eval baselines, especially if results look suspicious.

Agent Analysis Scripts (NEW!)

For agent-based evaluation results (Python vs AILANG comparisons with Claude Code):

1. Agent KPIs - Minimize Tokens & Turns

Shows efficiency metrics for agent runs - key for optimizing language and prompts.

.claude/skills/eval-analyzer/scripts/agent_kpis.sh eval_results/WITH_ALL_FIXES

Output:

Average turns, tokens, cost by language (Python vs AILANG)
Most expensive benchmarks (by turns) - candidates for optimization
Most efficient benchmarks - learn from these
Success rates and performance comparison

Goal: Minimize agent turns and tokens → indicates clearer prompts and simpler language.

2. Agent Transcripts - View AILANG Conversations

View full agent conversation logs to understand what happened.

# View all transcripts
.claude/skills/eval-analyzer/scripts/agent_transcripts.sh eval_results/WITH_ALL_FIXES

# View only failures
.claude/skills/eval-analyzer/scripts/agent_transcripts.sh eval_results/WITH_ALL_FIXES --failed-only

# View specific benchmark
.claude/skills/eval-analyzer/scripts/agent_transcripts.sh eval_results/WITH_ALL_FIXES fizzbuzz

Output:

Turn-by-turn conversation showing agent's thought process
Metrics: turns, tokens, duration
Success/failure status with error category
First 100 lines of transcript (with hint to view full)

Use for: Understanding why AILANG solutions fail or take many turns.

3. Python vs AILANG Comparison

Use the existing tools/compare_agents.sh script for side-by-side comparison:

./tools/compare_agents.sh eval_results/WITH_ALL_FIXES

Output:

Side-by-side metrics table
Solution code comparison
Transcripts for failed solutions (automatic)
Winner indicators for each metric

Standard Eval Workflow (Non-Agent)

Step 1: Get High-Level Overview

# Show overall statistics
ailang eval-matrix eval_results/baselines/v0.3.16 0.3.16 | head -60

Look for:

Overall success rate (target: >60%)
AILANG vs Python gap (current: ~54%)
Model performance variance
Top error codes

Step 2: Identify Problem Areas

# Categorize all failures
ailang eval-analyze -results eval_results/baselines/v0.3.16 -dry-run

Key metrics:

compile_error frequency (parse/syntax issues)
logic_error frequency (wrong output)
runtime_error frequency (crashes)
Which benchmarks fail most

Step 3: Query with jq (Custom Analysis)

Use jq queries on summary.jsonl for custom analysis:

# Ensure summary exists
ailang eval-summary eval_results/baselines/v0.3.20

# AILANG-only success rate (all models)
jq -s 'map(select(.lang == "ailang")) |
  {total: length, success: (map(select(.stdout_ok == true)) | length),
   rate: ((map(select(.stdout_ok == true)) | length) * 100.0 / length)}' \
  eval_results/baselines/v0.3.20/summary.jsonl

# Dev models only (useful for prompt testing)
jq -s 'map(select(.lang == "ailang" and
  (.model == "gpt5-mini" or .model == "claude-haiku-4-5" or .model == "gemini-2-5-flash"))) |
  {total: length, success: (map(select(.stdout_ok == true)) | length),
   rate: ((map(select(.stdout_ok == true)) | length) * 100.0 / length)}' \
  eval_results/baselines/v0.3.20/summary.jsonl

# Check specific benchmark across all models
jq -s 'map(select(.benchmark == "explicit_state_threading" and .lang == "ailang")) |
  map({model, success: .stdout_ok, error: .error_category})' \
  eval_results/baselines/v0.3.20/summary.jsonl

# Compare two versions (dev models AILANG-only)
jq -s 'map(select(.lang == "ailang" and
  (.model == "gpt5-mini" or .model == "claude-haiku-4-5" or .model == "gemini-2-5-flash"))) |
  {total: length, success: (map(select(.stdout_ok == true)) | length),
   rate: ((map(select(.stdout_ok == true)) | length) * 100.0 / length)}' \
  eval_results/baselines/v0.3.20/summary.jsonl \
  eval_results/baselines/v0.3.21/summary.jsonl

For more jq patterns, see resources/jq_queries.md

Step 4: Deep Dive with Helper Scripts

Use the provided helper scripts for detailed code inspection:

# Failure analysis with error categorization
.claude/skills/eval-analyzer/scripts/analyze_failures.sh eval_results/baselines/v0.3.16

# Model performance comparison
.claude/skills/eval-analyzer/scripts/compare_models.sh eval_results/baselines/v0.3.16

# Examine specific benchmark failures
.claude/skills/eval-analyzer/scripts/examine_code.sh eval_results/baselines/v0.3.16 api_call_json

Step 4: Compare with Previous Version

# Show regressions and improvements
ailang eval-compare eval_results/baselines/v0.3.15 eval_results/baselines/v0.3.16

Step 5: Generate Insights

Based on the data, identify:

Systemic Issues: Categories with >50 failures
Model Patterns: Which models struggle with which features
Benchmark Hotspots: Benchmarks with 100% failure rate
Cost Efficiency: Which models give best success/cost ratio
Trends: Improvements or regressions vs previous version

Key Metrics to Track

Overall Success Rate: AILANG vs Python gap (target: reduce below 50%)
Error Code Distribution:
- PAR_001 (parse errors) - indicates prompt/syntax issues
- WRONG_LANG - models writing Python instead of AILANG
- IMPERATIVE - models using imperative patterns
Model Performance: Which models work best with AILANG
Benchmark-Level: Which benchmarks consistently fail
Cost Efficiency: Success rate per dollar spent
Repair Success: Is self-repair helping? (currently low)

Common Issues

Issue 1: "Total Runs: 6" instead of 408

Symptom: eval-analyze only finds 6 results

Cause: Used positional argument instead of -results flag

Solution:

# ❌ WRONG
ailang eval-analyze eval_results/baselines/v0.3.16

# ✅ CORRECT
ailang eval-analyze -results eval_results/baselines/v0.3.16

Issue 2: Summary file not found

Symptom: jq queries fail with "file not found"

Cause: Need to run eval-summary first

Solution:

ailang eval-summary eval_results/baselines/v0.3.16

Issue 3: Design docs not generated

Symptom: eval-analyze shows issues but doesn't create docs

Cause: Using -dry-run flag

Solution: Run without -dry-run to generate design docs

Helper Scripts

The skill includes helper scripts in scripts/ directory:

quick_summary.sh

Fast overview using eval-matrix.

.claude/skills/eval-analyzer/scripts/quick_summary.sh eval_results/baselines/v0.3.16

Output: Overall stats, model performance, language breakdown, top error codes.

analyze_failures.sh

Detailed failure analysis with error categorization.

.claude/skills/eval-analyzer/scripts/analyze_failures.sh eval_results/baselines/v0.3.16 ailang

Output: Overall statistics, error categories, top failing benchmarks, model performance, error codes.

compare_models.sh

Model-by-model performance comparison.

.claude/skills/eval-analyzer/scripts/compare_models.sh eval_results/baselines/v0.3.16

Output: Success rates, first-attempt vs final, cost analysis, token usage, best model per benchmark.

examine_code.sh

Inspect generated code from specific benchmarks.

.claude/skills/eval-analyzer/scripts/examine_code.sh eval_results/baselines/v0.3.16 api_call_json
.claude/skills/eval-analyzer/scripts/examine_code.sh eval_results/baselines/v0.3.16 api_call_json gpt5

Output: Generated code, compiler errors, success status, error codes for each model run.

examine_prompts.sh

View prompts used for specific benchmarks.

.claude/skills/eval-analyzer/scripts/examine_prompts.sh eval_results/baselines/v0.3.16 api_call_json

Output: System prompt, user prompt, success status for benchmark runs.

verify_prompt_accuracy.sh

Check if prompt documentation matches actual implementation.

.claude/skills/eval-analyzer/scripts/verify_prompt_accuracy.sh v0.3.16

Output: Reports false limitations, undocumented features, and prompt-code mismatches.

Use this: After creating new prompt versions to catch documentation bugs!

Resources

Analysis Documents

resources/failure_analysis_v0.3.16.md - Comprehensive analysis of v0.3.16 eval results with root cause analysis

Common jq Patterns

See resources/jq_queries.md for more query examples and patterns.

Progressive Disclosure

This skill loads information progressively:

Always loaded: This SKILL.md file (workflow + commands + scripts)
Execute as needed: ailang eval-* commands and helper scripts
Load on demand: resources/jq_queries.md, analysis documents

Notes

All eval commands work offline (no API calls for analysis)
eval-analyze generates design docs using LLM (default: gpt5)
Summary JSONL format is stable and queryable
Use -dry-run to preview before generating design docs
baseline directories typically at eval_results/baselines/vX.X.X/
This skill complements post-release skill (which runs baselines)

Eval Analyzer

Eval Analyzer

Quick Start

When to Use This Skill

Key Eval Commands

1. Quick Overview - eval-matrix

2. Detailed Analysis - eval-analyze

3. Query-Friendly Summary - eval-summary

4. Compare Versions - eval-compare

5. Fair Comparison (RECOMMENDED) - fair_comparison.py

6. Validate Results - validate_eval_results.py

Agent Analysis Scripts (NEW!)

1. Agent KPIs - Minimize Tokens & Turns

2. Agent Transcripts - View AILANG Conversations

3. Python vs AILANG Comparison

Standard Eval Workflow (Non-Agent)

Step 1: Get High-Level Overview

Step 2: Identify Problem Areas

Step 3: Query with jq (Custom Analysis)

Step 4: Deep Dive with Helper Scripts

Step 4: Compare with Previous Version

Step 5: Generate Insights

Key Metrics to Track

Common Issues

Issue 1: "Total Runs: 6" instead of 408

Issue 2: Summary file not found

Issue 3: Design docs not generated

Helper Scripts

quick_summary.sh

analyze_failures.sh

compare_models.sh

examine_code.sh

examine_prompts.sh

verify_prompt_accuracy.sh

Resources

Analysis Documents

Common jq Patterns

Progressive Disclosure

Notes

1. Quick Overview - `eval-matrix`

2. Detailed Analysis - `eval-analyze`

3. Query-Friendly Summary - `eval-summary`

4. Compare Versions - `eval-compare`

5. Fair Comparison (RECOMMENDED) - `fair_comparison.py`

6. Validate Results - `validate_eval_results.py`